Update eval_README.md

docs/m4t/eval_README.md

@@ -2,7 +2,7 @@
 Refer to the [inference tutorial](../../scripts/m4t/predict/README.md) for detailed guidance on how to run inference using SeamlessM4T models. In this tutorial, the evaluation protocol used for all tasks supported by SeamlessM4T is briefly described.
 
 ### S2TT
-Sacrebleu library is used to compute the BLEU scores. To be consistent with Whisper, a character-level(*char*) tokenizer for Mandarin Chinese (cmn), Japanese (jpn), Thai (tha), Lao (lao), and Burmese (mya) is used, and the default *13a* tokenizer is used for other languages. Raw references and predictions are used for score computation and no normalization is applied.
+Sacrebleu library is used to compute the BLEU scores. To be consistent with Whisper, a character-level(*char*) tokenizer for Mandarin Chinese (cmn), Japanese (jpn), Thai (tha), Lao (lao), and Burmese (mya) is used. The default *13a* tokenizer is used for other languages. Raw (unnormalized) references and predictions are used for computing the scores.
 
 ```python
 import sacrebleu
@@ -12,22 +12,22 @@ bleu_score = bleu_metric.corpus_score(<PREDICTIONS>, [<REFERENCES>])
 ```
 
 ### S2ST and T2ST
-To measure the quality of the translated speech outputs, the audios are first transcribed using Whisper ASR model and later run sacrebleu on the ASR transcriptions comparing with the ground truth text references to compute the ASR-BLEU metric.
+To measure the quality of the translated speech outputs, the audios are first transcribed using Whisper ASR model and BLEU score is computed on these ASR transcriptions comparing them with the ground truth text references.
 
-Whisper large-v2 is used for non-English directions and medium.en trained on English-only data is used for English due to its superior performance.
+Whisper large-v2 is used for non-English target languages and medium.en trained on English-only data is used for English due to its superior performance.
 ```python
 import whisper
 
 model = whisper.load_model('medium.en')
 model = whisper.load_model('large-v2')
 ```
-To reproduce the transcriptions and there-by the ASR-BLEU scores, the language information is passed and the temperature, beam values are preset.
+To reproduce the whisper transcriptions and thereby the ASR-BLEU scores, greedy decoding is used with a preset temperature value of 0. Target language information is also passed to the whisper model. 
 
 ```python
 prediction = model.transcribe(<AUDIO_PATH>, language=<LANGUAGE>, temperature=0, beam_size=1)["text"]
 ```
 
-Whisper-normalizer is run on the ground truth <REFERENCES> and the model generated <PREDICTIONS> and score computation protocol for S2TT is followed to get the S2ST ASR-BLEU score
+Whisper-normalizer is run on the ground truth <REFERENCES> and the model generated <PREDICTIONS>. ASR-BLEU scores are computed using sacrebleu following the same tokenization as described for S2TT.
 ```python
 from whisper_normalizer.basic import BasicTextNormalizer
 
@@ -36,7 +36,7 @@ normalizer = BasicTextNormalizer()  ## For non-English directions
 ```
 
 ### T2TT
-Similar to S2TT, raw references and predictions are used to compute the chrf++ scores for text translation task and no normalization is applied.
+Similar to S2TT, raw(unnormalized) references and predictions are used to compute the chrf++ scores for text translation task.
 
 ```python
 import sacrebleu
@@ -44,10 +44,9 @@ import sacrebleu
 chrf_metric = sacrebleu.CHRF(word_order=2)
 chrf_score = chrf_metric.corpus_score(<REFERENCES>,<PREDICTIONS>)
 ```
-The spBLEU scores are reported for T2TT by using *flores200* tokenizer in sacrebleu.
 
 ### ASR
-Similar to Whisper, character-level error rates (CER) is used for Mandarin Chinese (cmn), Japanese (jpn), Thai (tha), Lao (lao), and Burmese (mya), languages and word-level error rates (WER) is used for the remaining languages. Whisper-normalizer is applied on the references & predictions and the `jiwer` library is used to compute the CER and WER scores.
+Similar to Whisper, character-level error rate (CER) metric is used for Mandarin Chinese (cmn), Japanese (jpn), Thai (tha), Lao (lao), and Burmese (mya) languages. Word-level error rate (WER) metric is used for the remaining languages. Whisper-normalizer is applied on the ground truth <REFERENCES> and the model generated <PREDICTIONS>. `Jiwer` library is used to compute these CER and WER scores.
 ```python
 import jiwer